Fuzzy adaptive learning control network with on-line neural learning
Fuzzy Sets and Systems - Special issue on fuzzy control
Neural Information Processing: Research and Development
Neural Information Processing: Research and Development
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Adaptive iterative learning control for robot manipulators
Automatica (Journal of IFAC)
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks
IEEE Transactions on Neural Networks
Effects of moving the center's in an RBF network
IEEE Transactions on Neural Networks
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In this paper, real time motion tracking of a robot manipulator based on the adaptive learning radial basis function network is proposed. This method for adaptive learning needs little knowledge of the plant in the design processes. So the centers and widths of the employed radial basis function network (RBFN) as well as the weights are determined adaptively. With the help of the RBFN, motion tracking of the robot manipulator is implemented without knowing the information of the system in advance. Furthermore, identification error and the tuned parameters of the RBFN are guaranteed to be uniformly ultimately bounded in the sense of Lyapunov's stability criterion.